An Image Classification Algorithm Based on HSV and 2-D Maximum Entropy Model

被引：0
|
作者：
Wang, Yanlin [1 ]
机构：
[1] Wuhan Business Univ, Informat Engn Coll, Wuhan, Hubei, Peoples R China
来源：
2017 INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS, ELECTRONICS AND CONTROL (ICCSEC) | 2017年
关键词：
component; formatting; style; styling; insert; SCALE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A 2-D maximum entropy model is proposed to overcome the weakness of BOVM (bag of visual words) histogram which always neglects the image color information. The 2-D maximum entropy mode of a class of image is built by H component and S component in HSI color space, and what's more, the corresponding 2-D maximum entropy distribution is the bottom reference feature vectors, which is used to match with an input image by Euclidian criterion in image classification algorithm. Experiments has illustrated that the algorithm presented in this paper has a higher image precision than the classification algorithm based on BOVM.
引用
收藏
页码：958 / 962
页数：5
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